This project enhances a Reactome-based RAG chatbot by adding query un…#9
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shivanshuyadav921 wants to merge 1 commit intoreactome:mainfrom
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This project enhances a Reactome-based RAG chatbot by adding query un…#9shivanshuyadav921 wants to merge 1 commit intoreactome:mainfrom
shivanshuyadav921 wants to merge 1 commit intoreactome:mainfrom
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…derstanding for better retrieval and a citation verification layer to ensure scientifically accurate, verifiable answers. This project develops an enhanced RAG-based chatbot for biological pathway analysis using Reactome as the knowledge source. The system improves traditional RAG pipelines by introducing two key components: Query Understanding Layer — Uses an LLM to classify user intent and decompose complex queries into smaller, independent sub-queries. This enables more precise and multi-step retrieval of relevant biological pathways, reactions, and relationships. Citation & Verification Layer — Ensures that all generated answers are grounded in Reactome data by enforcing citation of pathway IDs (e.g., R-HSA-XXXXX). Extracted IDs are validated using the Reactome API, and missing citations are automatically injected based on retrieved context. By combining LLM reasoning with structured biological knowledge retrieval and verification, the system produces accurate, explainable, and scientifically reliable answers, addressing key limitations of standard LLM-based systems such as hallucination and lack of traceability.
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understanding for better retrieval and a citation verification layer to ensure scientifically accurate, verifiable answers.
This project develops an enhanced RAG-based chatbot for biological pathway analysis using Reactome as the knowledge source. The system improves traditional RAG pipelines by introducing two key components:
Query Understanding Layer — Uses an LLM to classify user intent and decompose complex queries into smaller, independent sub-queries. This enables more precise and multi-step retrieval of relevant biological pathways, reactions, and relationships. Citation & Verification Layer — Ensures that all generated answers are grounded in Reactome data by enforcing citation of pathway IDs (e.g., R-HSA-XXXXX). Extracted IDs are validated using the Reactome API, and missing citations are automatically injected based on retrieved context.
By combining LLM reasoning with structured biological knowledge retrieval and verification, the system produces accurate, explainable, and scientifically reliable answers, addressing key limitations of standard LLM-based systems such as hallucination and lack of traceability.